MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with 10 million rows and 500 features. The target variable is binary. The dataset is stored in an Amazon S3 bucket. The data scientist wants to quickly identify which features have the highest correlation with the target variable. Which approach is MOST efficient?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon SageMaker Data Wrangler to import the dataset from S3 and generate a correlation matrix.
Amazon SageMaker Data Wrangler can directly import the dataset from S3 and generate a correlation matrix efficiently without needing to write custom code, making it the most efficient approach for identifying feature correlations with the target variable. Option B is incorrect because using Amazon QuickSight to create scatter plots for each of the 500 features would be time-consuming and not scalable. Option C is incorrect because Amazon Athena uses SQL queries which are not designed to compute correlation coefficients efficiently across a large number of features. Option D is incorrect because AWS Glue ETL is intended for data transformation pipelines and is not suitable for quick interactive correlation analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Amazon SageMaker Data Wrangler to import the dataset from S3 and generate a correlation matrix.
Why this is correct
Data Wrangler provides interactive data exploration and correlation analysis.
- ✗
Use Amazon QuickSight to create scatter plots for each feature vs. target.
Why it's wrong here
QuickSight is for visualization, not automated correlation computation.
- ✗
Use Amazon Athena with SQL queries to compute correlation coefficients.
Why it's wrong here
Athena does not support correlation functions natively.
- ✗
Use AWS Glue ETL to compute pairwise correlations and output to Amazon Redshift.
Why it's wrong here
Glue is for ETL, not interactive exploration.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.